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Practical AI Roadmap Workbook for Business Executives

A simple, practical workbook showing the real areas where AI adds value — and where it doesn’t.
The Dev Guys — Built with clarity, speed, and purpose.

Purpose of This Workbook

Modern business leaders face pressure to adopt AI strategies. Everyone seems to be experimenting with, buying, or promoting something AI-related. But many non-technical leaders are caught between extremes:
• Saying “yes” to every vendor or internal idea, hoping some of it will succeed.
• Saying “no” to everything because it feels risky or confusing.

It provides a third, smarter path — a clear, grounded way to find genuine AI opportunities.

You don’t have to be technical; you just need to know your operations well. AI is only effective when built on your existing processes.

How to Use This Workbook

Either fill it solo or discuss it collaboratively. It’s not about completion — it’s about clarity. By the end, you’ll have:
• A short list of meaningful AI opportunities tied to profit or efficiency.
• Understanding of where AI should not be used.
• A structured sequence of projects instead of random pilots.

Think of it as a guide, not a form. A good roadmap fits on one slide and makes sense to your CFO.

AI strategy equals good business logic, simply expressed.

Step 1 — Business First

Begin with Results, Not Technology

Most AI discussions begin with tools and tech questions like “Can we use ChatGPT here?” — that’s backward. Instead, begin with clear results that matter to your company.

Ask:
• What top objectives are driving your business now?
• Where are teams overworked or error-prone?
• Where do poor data or slow insights hold back progress?

It should improve something tangible — speed, accuracy, or cost. If an idea doesn’t tie to these, it’s not a roadmap — it’s just an experiment.

Leaders who skip this step collect shiny tools; those who follow it build lasting leverage.

Step Two — Map the Workflows

Visualise the Process, Not the Platform

You must see the true flow of tasks, not the idealised version. Pose one question: “What happens between X starting and Y completing?”.

Examples include:
• Lead comes in ? assigned ? follow-up ? quote ? revision ? close/lost.
• Support ticket ? triaged ? answered ? escalated ? resolved.
• Invoice generated ? sent ? reminded ? paid.

Each step has three parts: inputs, actions, outputs. AI adds value where inputs are messy, actions are repetitive, and outputs are predictable.

Rank and Select AI Use Cases

Evaluate Each Use Case for Business Value

Evaluate AI ideas using a simple impact vs effort grid.

Use a mental 2x2 chart — impact vs effort.
• Focus first on small, high-impact changes.
• Big strategic initiatives take time but deliver scale.
• Nice-to-Haves — low impact, low effort.
• Delay ideas that drain resources without impact.

Consider risk: some actions are reversible, others are not.

Small wins set the foundation for larger bets.

Laying Strong Foundations

Data Quality Before AI Quality

AI projects fail more from poor data than bad models. Clarity first, automation later.

Design Human-in-the-Loop by Default

AI should draft, suggest, or monitor — not act blindly. Build confidence MVP Rescue before full automation.

Common Traps

Steer Clear of Predictable Failures

01. The Demo Illusion — excitement without strategy.
02. The Pilot Problem — learning without impact.
03. The Full Automation Fantasy — imagining instant department replacement.

Choose disciplined execution over hype.

Partnering with Vendors and Developers

Frame problems, don’t build algorithms. State outcomes clearly — e.g., “reduce response time 40%”. Share messy data and edge cases so tech partners understand reality. Agree on success definitions and rollout phases.

Request real-world results, not sales pitches.

Evaluating AI Health

Indicators of a Balanced AI Plan

It’s simple, measurable, and owned.
Buzzword-free alignment is visible.
Ownership and clarity drive results.

Essential Pre-Launch AI Questions

Before any project, confirm:
• What measurable result does it support?
• Which workflow is involved, and can it be described simply?
• Do we have data and process clarity?
• Where will humans remain in control?
• What is the 3-month metric?
• What’s the fallback insight?

Conclusion

Good AI brings order, not confusion. It’s not a list of tools — it’s an execution strategy. True AI integration supports your business invisibly.